SoroushVahidi/scidocs huggingface dataset:
The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain
(1999)

Little is known about the function of the retrosplenial cortex and until recently, there was no evidence that it had any involvement in emotional processes. Surprisingly, recent functional neuroimaging studies show that the retrosplenial cortex is consistently activated by emotionally salient words. A review of the functional neuroimaging literature reveals a previously overlooked pattern of observations: the retrosplenial cortex is the cortical region most consistently activated by emotionally salient stimuli. Evidence that this region is also involved in episodic memory suggests that it might have a role in the interaction between emotion and episodic memory. Recognition that the retrosplenial cortex has a prominent role in the processing of emotionally salient stimuli invites further studies to define its specific functions and its interactions with other emotion-related brain regions.
Comparator Networks
(2018)

The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets. Instead, we design a neural network architecture that can directly learn set-wise verification. Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair – this involves attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage highquality representations for each set, internal competition is introduced for recalibration based on the landmark score; (iii) Inspired by image retrieval, a novel hard sample mining regime is proposed to control the sampling process, such that the DCN is complementary to the standard image classification models. Evaluations on the IARPA Janus face recognition benchmarks show that the comparator networks outperform the previous state-of-the-art results by a large margin.
Fingertip friction modulation due to electrostatic attraction
(2013)

The human fingertip is extremely sensitive to lateral (shear) forces that arise in surface exploration. We and others have developed haptic displays that work by modulating surface friction via electrostatic attraction. Despite the demonstrated ability of these displays to render haptic environments, an understanding of the fingertip-surface interface is lacking. We have developed a tribometer for measuring the lateral frictional forces on a fingertip under well-controlled conditions. We show an expected square law dependence of frictional force (and inferred electrostatic normal force) on actuation voltage, although we observe a large person to person variability. We model an expected dependence of the frictional force on the frequency of the actuation voltage, predicting a first order cut off below about 500Hz. However, our measurements are unambiguously at odds with the model's predictions.
Understanding the HyperLogLog : a Near-Optimal Cardinality Estimation Algorithm
(2016)

The HyperLogLog algorithm (HLL) is a method to estimate the number of distinct elements in large datasets i.e. cardinality, in a single pass, and using a very small amount of memory. The HLL algorithm is an optimization of the method presented in 2003 by Durand and Flajolet in the paper LogLog Counting of Large Cardinalities. In this report we analyze the so called cornerstone of Big Data infrastructures, give a detailed description of the algorithm and explain the mathematical intuition behind it.
Semi-supervised Named Entity Recognition in noisy-text
(2016)

Many of the existing Named Entity Recognition (NER) solutions are built based on news corpus data with proper syntax. These solutions might not lead to highly accurate results when being applied to noisy, user generated data, e.g., tweets, which can feature sloppy spelling, concept drift, and limited contextualization of terms and concepts due to length constraints. The models described in this paper are based on linear chain conditional random fields (CRFs), use the BIEOU encoding scheme, and leverage random feature dropout for up-sampling the training data. The considered features include word clusters and pre-trained distributed word representations, updated gazetteer features, and global context predictions. The latter feature allows for ingesting the meaning of new or rare tokens into the system via unsupervised learning and for alleviating the need to learn lexicon based features, which usually tend to be high dimensional. In this paper, we report on the solution [ST] we submitted to the WNUT 2016 NER shared task. We also present an improvement over our original submission [SI], which we built by using semi-supervised learning on labelled training data and pre-trained resourced constructed from unlabelled tweet data. Our ST solution achieved an F1 score of 1.2% higher than the baseline (35.1% F1) for the task of extracting 10 entity types. The SI resulted in an increase of 8.2% in F1 score over the baseline (7.08% over ST). Finally, the SI model’s evaluation on the test data achieved a F1 score of 47.3% (~1.15% increase over the 2 best submitted solution). Our experimental setup and results are available as a standalone twitter NER tool at https://github.com/napsternxg/TwitterNER.
The web impact factor: a critical review
(2006)

Purpose – We analyse the link-based web site impact measure known as the Web Impact Factor (WIF). It is a quantitative tool for evaluating and ranking web sites, top-level domains and sub-domains. We also discuss the WIF's advantages and disadvantages, data collection problems, and validity and reliability of WIF results. Design/methodology/approach – A key to webometric studies has been the use of largescale search engines, such as Yahoo and AltaVista that allow measurements to be made of the total number of pages in a web site and the total number of backlinks to the web site. These search engines provide similar possibilities for the investigation of links between web sites/pages to those provided by the academic journals citation databases from the Institute of Scientific Information (ISI). But the content of the Web is not of the same nature and quality as the databases maintained by the ISI. Findings – This paper reviews how the WIF has been developed and applied. It has been suggested that Web Impact Factors can be calculated as a way of comparing the attractiveness of web sites or domains on the Web. It is concluded that, while the WIF is arguably useful for quantitative intra-country comparison, application beyond this (i.e., to inter-country assessment) has little value. Originality/value – The paper attempts to make a critical review over literature on the WIF and associated indicators.
Stock-out Prediction in Multi-echelon Networks
(2017)

In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and in many other time periods. For example, a supply disruption upstream will have an effect on downstream, customer-facing nodes several periods later as the disruption "cascades" through the system. There is very little research on stock-out prediction in single-echelon systems and (to the best of our knowledge) none on multi-echelon systems. However, in real the world, it is clear that there is significant interest in techniques for this sort of stock-out prediction. Therefore, our research aims to fill this gap by using deep neural networks (DNN) to predict stock-outs in multi-echelon supply chains.
Detection of Occluding Contours and Occlusion by Active Binocular Stereo
(1993)

|We propose a reliable method to detect occluding contours and occlusion by active binocular stereo with an occluding contour model that describes the correspondence between the contour of curved objects and its projected image. Applying the image ow generated by moving one camera to the model, we can restrict possible matched points seen by the other camera. We detect occluding contours by tting these matched points to the model. This method can nd occluding contours and occlusion more reliably than conventional ones because stereo matching is performed by using the geometric constraint based on the occluding contour model instead of heuristic constraints. Experimental results of the proposed method applied to the actual scene are presented.
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
(2017)

While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.
Salt resistance genes revealed by functional metagenomics from brines and moderate-salinity rhizosphere within a hypersaline environment
(2015)

Hypersaline environments are considered one of the most extreme habitats on earth and microorganisms have developed diverse molecular mechanisms of adaptation to withstand these conditions. The present study was aimed at identifying novel genes from the microbial communities of a moderate-salinity rhizosphere and brine from the Es Trenc saltern (Mallorca, Spain), which could confer increased salt resistance to Escherichia coli. The microbial diversity assessed by pyrosequencing of 16S rRNA gene libraries revealed the presence of communities that are typical in such environments and the remarkable presence of three bacterial groups never revealed as major components of salt brines. Metagenomic libraries from brine and rhizosphere samples, were transferred to the osmosensitive strain E. coli MKH13, and screened for salt resistance. Eleven genes that conferred salt resistance were identified, some encoding for well-known proteins previously related to osmoadaptation such as a glycerol transporter and a proton pump, whereas others encoded proteins not previously related to this function in microorganisms such as DNA/RNA helicases, an endonuclease III (Nth) and hypothetical proteins of unknown function. Furthermore, four of the retrieved genes were cloned and expressed in Bacillus subtilis and they also conferred salt resistance to this bacterium, broadening the spectrum of bacterial species in which these genes can function. This is the first report of salt resistance genes recovered from metagenomes of a hypersaline environment.
Process modeling for industry 4.0 applications: Towards an industry 4.0 process modeling language and method
(2016)

The term Industry 4.0 derives from the new (fourth) industrial revolution enabling suppliers and manufacturers to leverage new technological concepts like Internet of Things, Big Data, and Cloud Computing: New or enhanced products and services can be created, cost be reduced and productivity be increased. Similar terms are Smart Factory or Smart Manufacturing. The ideas, concepts and technologies are not hype anymore - they are at least partly reality, but many software specification and development aspects are still not sufficiently covered, e.g. standardization, specification and modeling languages. This paper presents an Industry 4.0 process modeling language (I4PML) that is an extension (UML profile with stereotypes) of OMG's BPMN (Business Process Model and Notation) standard. We also describe a method for the specification of Industry 4.0 applications using UML and I4PML.
Strategies for Evaluating Software Usability
(1998)

This paper presents a usage analysis and taxonomy of methods which are used to evaluate the usability of computer systems. To accommodate the analysis and taxonomy, a matrix of strategies which can be used for effective usability evaluation is presented. Such an analysis, taxonomy and strategies support human-computer interaction (HCI) professionals who have the responsibility for ensuring computer system usability. The strategies outlined are named Virtual Engineering, Soft Modelling, Hard Review and Real World. This paper also uses a composite set of existing popular generic evaluation methods which can be used as part of these strategies. The methods used are observation, questionnaire, interview, empirical methods, user groups, cognitive walkthroughs, heuristic methods, review methods and model methods. The paper continues by presenting a Usage Analysis Table of these methods and concludes by grouping them into a Taxonomy of Usability Evaluation Methods. A key emphasis of this paper is the appropriateness of individual methods to lifecycle timing.
Formal Query Generation for Question Answering over Knowledge Bases
(2018)

Question answering (QA) systems often consist of several components such as Named Entity Disambiguation (NED), Relation Extraction (RE), and Query Generation (QG). In this paper, we focus on the QG process of a QA pipeline on a large-scale Knowledge Base (KB), with noisy annotations and complex sentence structures. We therefore propose SQG, a SPARQL Query Generator with modular architecture, enabling easy integration with other components for the construction of a fully functional QA pipeline. SQG can be used on large open-domain KBs and handle noisy inputs by discovering a minimal subgraph based on uncertain inputs, that it receives from the NED and RE components. This ability allows SQG to consider a set of candidate entities/relations, as opposed to the most probable ones, which leads to a significant boost in the performance of the QG component. The captured subgraph covers multiple candidate walks, which correspond to SPARQL queries. To enhance the accuracy, we present a ranking model based on Tree-LSTM that takes into account the syntactical structure of the question and the tree representation of the candidate queries to find the one representing the correct intention behind the question. SQG outperforms the baseline systems and achieves a macro F1-measure of 75% on the LC-QuAD dataset.
ECONOMICS AND IDENTITY
(2000)

This paper considers how identity, a person’s sense of self, affects economic outcomes. We incorporate the psychology and sociology of identity into an economic model of behavior. In the utility function we propose, identity is associated with different social categories and how people in these categories should behave. We then construct a simple game-theoretic model showing how identity can affect individual interactions. The paper adapts these models to gender discrimination in the workplace, the economics of poverty and social exclusion, and the household division of labor. In each case, the inclusion of identity substantively changes conclusions of previous economic analysis.
10 An Empirical Approach for the Evaluation of Voice User Interfaces
(2012)

Nowadays, the convergence of devices, electronic computing, and massive media produces huge volumes of information, which demands the need for faster and more efficient interaction between users and information. How to make information access manageable, efficient, and easy becomes the major challenge for Human-Computer Interaction (HCI) researchers. The different types of computing devices, such as PDAs (personal digital assistants), tablet PCs, desktops, game consoles, and the next generation phones, provide many different modalities for information access. This makes it possible to dynamically adapt application user interfaces to the changing context. However, as applications go more and more pervasive, these devices show theirs limited input/output capacity caused by small visual displays, use of hands to operate buttons and the lack of an alphanumeric keyboard and mouse (Gu & Gilbert, 2004). Voice User Interface (VUI) systems are capable of, besides recognizing the voice of their users, to understand voice commands, and to provide responses to them, usually, in real time. The state-of-the-art in speech technology already allows the development of automatic systems designed to work in real conditions. VUI is perhaps the most critical factor in the success of any automated speech recognition (ASR) system, determining whether the user experience will be satisfying or frustrating, or even whether the customer will remain one. This chapter describes a practical methodology for creating an effective VUI design. The methodology is scientifically based on principles in linguistics, psychology, and language technology (Cohen et al. 2004; San-Segundo et al., 2005). Given the limited input/output capabilities of mobile devices, speech presents an excellent way to enter and retrieve information either alone or in combination with other modalities. Furthermore, people with disabilities should be provided with a wide range of alternative interaction modalities other than the traditional screen-mouse based desktop computing devices. Whether the disability is temporary or permanent, people with reading difficulty, visual impairment, and/or any difficulty using a keyboard, or mouse can rely on speech as an alternate approach for information access. Source: User Interfaces, Book edited by: Rita Mátrai, ISBN 978-953-307-084-1, pp. 270, May 2010, INTECH, Croatia, downloaded from SCIYO.COM
Handwriting instruction in elementary schools.
(2006)

OBJECTIVE
Classroom teachers teach handwriting, but when problems arise, students are referred to occupational therapy for remediation. This study, conducted by occupational therapists, reviews handwriting instruction by classroom teachers in one school district.


METHOD
Teachers from kindergarten through grade 6 were asked to complete an open-ended questionnaire regarding handwriting instruction.


RESULTS
Teachers differed in their methods of instruction, including in the programs and paper used, and practice provided. Teachers of grades 5 and 6 had to continue to review handwriting instruction, because all students could not fluently use handwriting as a tool of expression.


CONCLUSION
Elementary students need structured instruction to develop the motor skill of writing. School-based occupational therapists can support effective handwriting instruction by interpreting information from motor learning theory pertaining to instruction and practice, which supports acquisition, transfer, and retention of handwriting skills. They also need to be cognizant of prior handwriting instruction when addressing handwriting difficulties.
From basic network principles to neural architecture: emergence of orientation columns.
(1986)

Orientation-selective cells--cells that are selectively responsive to bars and edges at particular orientations--are a salient feature of the architecture of mammalian visual cortex. In the previous paper of this series, I showed that such cells emerge spontaneously during the development of a simple multilayered network having local but initially random feedforward connections that mature, one layer at a time, according to a simple development rule (of Hebb type). In this paper, I show that, in the presence of lateral connections between developing orientation cells, these cells self-organize into banded patterns of cells of similar orientation. These patterns are similar to the "orientation columns" found in mammalian visual cortex. No orientation preference is specified to the system at any stage, none of the basic developmental rules is specific to visual processing, and the results emerge even in the absence of visual input to the system (as has been observed in macaque monkey).
From basic network principles to neural architecture: emergence of orientation-selective cells.
(1986)

This is the second paper in a series of three that explores the emergence of several prominent features of the functional architecture of visual cortex, in a "modular self-adaptive network" containing several layers of cells with parallel feedforward connections whose strengths develop according to a Hebb-type correlation-rewarding rule. In the present paper I show that orientation-selective cells, similar to the "simple" cortical cells of Hubel and Wiesel [Hubel, D. H. & Wiesel, T. N. (1962) J. Physiol. 160, 106-154], emerge in such a network. No orientation preference is specified to the system at any stage, the orientation-selective cell layer emerges even in the absence of environmental input to the system, and none of the basic developmental rules is specific to visual processing.
Auto-association by multilayer perceptrons and singular value decomposition
(1988)

The multilayer perceptron, when working in auto-association mode, is sometimes considered as an interesting candidate to perform data compression or dimensionality reduction of the feature space in information processing applications. The present paper shows that, for auto-association, the nonlinearities of the hidden units are useless and that the optimal parameter values can be derived directly by purely linear techniques relying on singular value decomposition and low rank matrix approximation, similar in spirit to the well-known Karhunen-Loève transform. This approach appears thus as an efficient alternative to the general error back-propagation algorithm commonly used for training multilayer perceptrons. Moreover, it also gives a clear interpretation of the rôle of the different parameters.
Exploratory analysis of OpenStreetMap for land use classification
(2013)

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas' values are remarkable and promising results that encourages us for future research on this topic.